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Record W2767169264 · doi:10.1108/jacpr-05-2017-0283

Campus sexual assault: examination of policy and research

2017· article· en· W2767169264 on OpenAlexaffabout
Unnati Patel, Ronald Roesch

Bibliographic record

VenueJournal of Aggression Conflict and Peace Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLegislationOriginalitySexual assaultIntervention (counseling)CriminologyPublic relationsAction (physics)PsychologyValue (mathematics)Political scienceSocial psychologySuicide preventionPoison controlMedicineLawEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Purpose Campus sexual assault has received a great deal of media attention in recent years, with much focus being placed on the factors unique to universities that enable these crimes to occur. The purpose of this paper is to discuss the circumstances under which these crimes take place and examine the policies of institutions across the USA and Canada to determine whether legislation from various governmental levels impacts the prevalence and incidence rates of sexual assault. Design/methodology/approach A review of the literature on sexual assault is conducted to gain an understanding of the contributory factors in campus sexual assault, and fields outside of psychology are included in the search to capture phenomena outside the perpetrator-victim dyad. Findings The findings suggest that unique variables exist in campus culture including prevention and intervention strategies put in place by governments and individual universities. Some of these policies are aimed at providing victim services, while others engage faculty, staff, and students in taking action from a bystander standpoint. Originality/value This paper also investigates the impacts that mandatory policies would have across North America, and suggests future policy initiatives to reduce the deleterious effects of sexual assault for students and universities alike.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.260
GPT teacher head0.543
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2017
Admission routes2
Has abstractyes

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